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Tan J.,Sun Yat Sen University | Tan J.,Guangdong Province Key Laboratory of Computational Science | Tan J.,Vins Information System Technology Co. | Guo X.,Sun Yat Sen University | And 4 more authors.
Computers and Education | Year: 2014

Teachers and students often consider learning programming a difficult pursuit. Face-to-face learning alone cannot provide effective teaching or efficient solutions for learning. A case teaching model can make students active in programming courses, even as it relies on solid learning theory and pedagogical strategies. This paper reports a study based on a case teaching model in C/C++ programming. The Laboratory Animal System (LAS) is a standalone case for management of laboratory animals. This paper includes an overview of LAS architectural design and user interface by C/C++ and presents the design, implementation, and evaluation of the model, as well as its implications for learning computer programming. The case method provides an interactive learning environment for students. Based on a survey of student feedback, students can learn C/C++ programming and gain knowledge more quickly and effectively using the case teaching model than through some traditional methods of teaching. © 2014 Elsevier Ltd. All rights reserved.

Chen W.,Sun Yat Sen University | Chen W.,Guangdong Province Key Laboratory of Computational Science | Feng G.,Sun Yat Sen University | Feng G.,Guangdong Province Key Laboratory of Computational Science
Neurocomputing | Year: 2012

Recently, graph-based spectral clustering algorithms have been developing rapidly, which are proposed as discrete combinatorial optimization problems and approximately solved by relaxing them into tractable eigenvalue decomposition problems. In this paper, we first review the current existing spectral clustering algorithms in a unified-framework way and give a straightforward explanation about spectral clustering. We also present a novel model for generalizing the unsupervised spectral clustering to semi-supervised spectral clustering. Under this model, prior information given by some instance-level constraints can be generalized to space-level constraints. We find that (undirected) graph built on the enlarged prior information is more meaningful, hence the boundaries of the clusters are more correct. Experimental results based on toy data, real-world data and image segmentation demonstrate the advantages of the proposed model. © 2011 Elsevier B.V.

Hu J.-F.,Sun Yat Sen University | Zheng W.-S.,Sun Yat Sen University | Zheng W.-S.,Guangdong Province Key Laboratory of Computational Science | Lai J.,Sun Yat Sen University | Zhang J.,University of Dundee
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2015

In this paper, we focus on heterogeneous feature learning for RGB-D activity recognition. Considering that features from different channels could share some similar hidden structures, we propose a joint learning model to simultaneously explore the shared and feature-specific components as an instance of heterogenous multi-task learning. The proposed model in an unified framework is capable of: 1) jointly mining a set of subspaces with the same dimensionality to enable the multi-task classifier learning, and 2) meanwhile, quantifying the shared and feature-specific components of features in the subspaces. To efficiently train the joint model, a three-step iterative optimization algorithm is proposed, followed by two inference models. Extensive results on three activity datasets have demonstrated the efficacy of the proposed method. In addition, a novel RGB-D activity dataset focusing on human-object interaction is collected for evaluating the proposed method, which will be made available to the community for RGB-D activity benchmarking and analysis. © 2015 IEEE.

Wu J.-S.,Sun Yat Sen University | Wu J.-S.,Guangdong Province Key Laboratory of Computational Science | Zheng W.-S.,Sun Yat Sen University | Zheng W.-S.,Guangdong Province Key Laboratory of Computational Science | Lai J.-H.,Sun Yat Sen University
IJCAI International Joint Conference on Artificial Intelligence | Year: 2013

By always mapping data from lower dimensional space into higher or even infinite dimensional space, kernel κ-means is able to organize data into groups when data of different clusters are not linearly separable. However, kernel κ-means incurs the large scale computation due to the representation theorem, i.e. keeping an extremely large kernel matrix in memory when using popular Gaussian and spatial pyramid matching kernels, which largely limits its use for processing large scale data. Also, existing kernel clustering can be overfitted by outliers as well. In this paper, we introduce an Euler clustering, which can not only maintain the benefit of nonlinear modeling using kernel function but also significantly solve the large scale computational problem in kernel-based clustering. This is realized by incorporating Euler kernel. Euler kernel is relying on a nonlinear and robust cosine metric that is less sensitive to outliers. More important it intrinsically induces an empirical map which maps data onto a complex space of the same dimension. Euler clustering takes these advantages to measure the similarity between data in a robust way without increasing the dimensionality of data, and thus solves the large scale problem in kernel κ-means. We evaluate Euler clustering and show its superiority against related methods on five publicly available datasets.

Hu J.-F.,Sun Yat Sen University | Zheng W.-S.,Sun Yat Sen University | Zheng W.-S.,Guangdong Province Key Laboratory of Computational Science | Lai J.,Sun Yat Sen University | And 2 more authors.
Proceedings of the IEEE International Conference on Computer Vision | Year: 2013

Human action can be recognised from a single still image by modelling Human-object interaction (HOI), which infers the mutual spatial structure information between human and object as well as their appearance. Existing approaches rely heavily on accurate detection of human and object, and estimation of human pose. They are thus sensitive to large variations of human poses, occlusion and unsatisfactory detection of small size objects. To overcome this limitation, a novel exemplar based approach is proposed in this work. Our approach learns a set of spatial pose-object interaction exemplars, which are density functions describing how a person is interacting with a manipulated object for different activities spatially in a probabilistic way. A representation based on our HOI exemplar thus has great potential for being robust to the errors in human/object detection and pose estimation. A new framework consists of a proposed exemplar based HOI descriptor and an activity specific matching model that learns the parameters is formulated for robust human activity recognition. Experiments on two benchmark activity datasets demonstrate that the proposed approach obtains state-of-the-art performance. © 2013 IEEE.

Zheng W.-S.,Sun Yat Sen University | Zheng W.-S.,Guangdong Province Key Laboratory of Computational Science | Gong S.,Queen Mary, University of London | Xiang T.,Queen Mary, University of London
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2016

Solving the problem of matching people across non-overlapping multi-camera views, known as person re-identification (re-id), has received increasing interests in computer vision. In a real-world application scenario, a watch-list (gallery set) of a handful of known target people are provided with very few (in many cases only a single) image(s) (shots) per target. Existing re-id methods are largely unsuitable to address this open-world re-id challenge because they are designed for (1) a closed-world scenario where the gallery and probe sets are assumed to contain exactly the same people, (2) person-wise identification whereby the model attempts to verify exhaustively against each individual in the gallery set, and (3) learning a matching model using multi-shots. In this paper, a novel transfer local relative distance comparison (t-LRDC) model is formulated to address the open-world person re-identification problem by one-shot group-based verification. The model is designed to mine and transfer useful information from a labelled open-world non-target dataset. Extensive experiments demonstrate that the proposed approach outperforms both non-transfer learning and existing transfer learning based re-id methods. © 1979-2012 IEEE.

Wu J.-S.,Sun Yat Sen University | Wu J.-S.,SYSU CMU Shunde International Joint Research Institute | Zheng W.-S.,Sun Yat Sen University | Zheng W.-S.,Guangdong Province Key Laboratory of Computational Science | And 2 more authors.
Neural Networks | Year: 2015

Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. © 2014 Elsevier Ltd.

Chen W.,Sun Yat Sen University | Chen W.,Guangdong Province Key Laboratory of Computational Science | Feng G.,Sun Yat Sen University | Feng G.,Guangdong Province Key Laboratory of Computational Science
Knowledge-Based Systems | Year: 2012

Recently, many k-way spectral clustering algorithms have been proposed, satisfying one or both of the following requirements: between-cluster similarities are minimized and within-cluster similarities are maximized. In this paper, a novel graph-based spectral clustering algorithm called discriminant cut (Dcut) is proposed, which first builds the affinity matrix of a weighted graph and normalizes it with the corresponding regularized Laplacian matrix, then partitions the vertices into k parts. Dcut has several advantages. First, it is derived from graph partition and has a straightforward geometrical explanation. Second, it emphasizes the above requirements simultaneously. Besides, it is computationally feasible because the NP-hard intractable graph cut problem can be relaxed into a mild eigenvalue decomposition problem. Toy-data and real-data experimental results show that Dcut is pronounced comparing with other spectral clustering methods. © 2011 Elsevier B.V. All rights reserved.

Zhu J.-Y.,Sun Yat Sen University | Zhu J.-Y.,Shunde International Joint Research Institute | Zheng W.-S.,Sun Yat Sen University | Zheng W.-S.,Guangdong Province Key Laboratory of Computational Science | And 4 more authors.
IEEE Transactions on Information Forensics and Security | Year: 2014

Visual versus near infrared (VIS-NIR) face image matching uses an NIR face image as the probe and conventional VIS face images as enrollment. It takes advantage of the NIR face technology in tackling illumination changes and low-light condition and can cater for more applications where the enrollment is done using VIS face images such as ID card photos. Existing VIS-NIR techniques assume that during classifier learning, the VIS images of each target people have their NIR counterparts. However, since corresponding VIS-NIR image pairs of the same people are not always available, which is often the case, so those methods cannot be applied. To address this problem, we propose a transductive method named transductive heterogeneous face matching (THFM) to adapt the VIS-NIR matching learned from training with available image pairs to all people in the target set. In addition, we propose a simple feature representation for effective VIS-NIR matching, which can be computed in three steps, namely Log-DoG filtering, local encoding, and uniform feature normalization, to reduce heterogeneities between VIS and NIR images. The transduction approach can reduce the domain difference due to heterogeneous data and learn the discriminative model for target people simultaneously. To the best of our knowledge, it is the first attempt to formulate the VIS-NIR matching using transduction to address the generalization problem for matching. Experimental results validate the effectiveness of our proposed method on the heterogeneous face biometric databases. © 2005-2012 IEEE.

Chen Y.,South China Agricultural University | Chen Y.,Guangzhou University | Zheng W.-S.,University of Science and Technology of China | Zheng W.-S.,Guangdong Province Key Laboratory of Computational Science | And 2 more authors.
Neural Networks | Year: 2013

High-dimensionality of data and the small sample size problem are two significant limitations for applying subspace methods which are favored by face recognition. In this paper, a new linear dimension reduction method called locally uncorrelated discriminant projections (LUDP) is proposed, which addresses the two problems from a new aspect. More specifically, we propose a locally uncorrelated criterion, which aims to decorrelate learned discriminant factors over data locally rather than globally. It has been shown that the statistical uncorrelation criterion is an important property for reducing dimension and learning robust discriminant projection as well. However, data are always locally distributed, so it is more important to explore locally statistical uncorrelated discriminant information over data. We impose this new constraint into a graph-based maximum margin analysis, so that LUDP also characterizes the local scatter as well as nonlocal scatter, seeking to find a projection that maximizes the difference, rather than the ratio between the nonlocal scatter and the local scatter. Experiments on ORL, Yale, Extended Yale face database B and FERET face database demonstrate the effectiveness of our proposed method. © 2013 Elsevier Ltd.

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